探究图像分割中不确定性分解的纠缠问题，提出量化方法并验证最佳组合
Rethinking Uncertainty Quantification and Entanglement in Image Segmentation
- 系统测试多种不确定性建模组合，揭示其交互影响
- 集成方法显著降低不确定性纠缠，表现最优
- 软最大值集成在各类任务中均表现突出，适合医疗等安全场景
不确定性量化（UQ）在医疗图像分割等安全关键应用中至关重要。通常将总不确定性分解为数据相关的认知不确定性（AU）和模型相关的似然不确定性（EU）。已有方法分别建模AU（如概率UNet、扩散模型）和EU（如集成、MC Dropout），但二者结合时的交互关系尚不明确。近期研究发现AU与EU存在显著纠缠，削弱了分解的可解释性与实用性。本文开展全面的实证研究，涵盖广泛的AU-EU模型组合，提出量化不确定性纠缠的指标，并在下游UQ任务中评估性能。结果表明：集成方法始终表现出更低的纠缠水平和更优性能；软最大值模型普遍优于其他AU方法，但在校准任务中表现依赖数据集；软最大值集成在所有任务中均表现优异。最后分析了不确定性纠缠的潜在成因，并提出缓解方向。
原文摘要 · Abstract (English)
Uncertainty quantification (UQ) is crucial in safety-critical applications such as medical image segmentation. Total uncertainty is typically decomposed into data-related aleatoric uncertainty (AU) and model-related epistemic uncertainty (EU). Many methods exist for modeling AU (such as Probabilistic UNet, Diffusion) and EU (such as ensembles, MC Dropout), but it is unclear how they interact when combined. Additionally, recent work has revealed substantial entanglement between AU and EU, undermining the interpretability and practical usefulness of the decomposition. We present a comprehensive empirical study covering a broad range of AU-EU model combinations, propose a metric to quantify uncertainty entanglement, and evaluate both across downstream UQ tasks. Ensembles consistently exhibit lower entanglement and superior performance. Softmax models usually beat other AU methods, except in calibration where the results are dataset-dependent. A softmax ensemble performs remarkably well on all tasks. Finally, we analyze potential sources of uncertainty entanglement and outline directions for mitigating this effect.
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